Information processing apparatus, information processing system, information processing method, program and recording medium

ABSTRACT

The present invention provides an information processing apparatus capable of accurately separating parenchymal cells and stromal cells from each other regardless of the staining intensity of the cells. The information processing apparatus is an information processing apparatus  100  including: an image processing unit  110  for smoothing a tissue sample image  150  obtained by staining and then imaging a biological tissue containing parenchymal cells  151  and stromal cells  152  so that luminance values of cell components of each of the parenchymal cells  151  become less than those of each of the stromal cells  152 ; and a mask generation unit  120  for generating, through generating a binary image by binarizing the tissue sample image  115  smoothed by the image processing unit  110 , a mask  125  for removing a region of the stromal cells from the tissue sample image  115.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a National Stage of International Application No.PCT/JP2011/071930 filed on Sep. 27, 2011, which claims priority fromJapanese Patent Application No. 2010-223049, filed on Sep. 30, 2010, thecontents of all of which are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

The present invention relates to an information processing apparatus, aninformation processing system, an information processing method, aprogram, and a recording medium.

BACKGROUND ART

As a technique for supporting a diagnosis based on a tissue sample imageof a biological tissue, a method in which a diagnosis is made throughstaining a section of a biological tissue and then observing the stateof the staining is known. It is important for the diagnosis to separatea stromal part from the tissue sample image after the staining. Thepatent document 1 describes a classification of stroma from a biologicaltissue through hematoxylin-eosin staining (HE staining). Specifically,the paragraph [0073] of the patent document 1 describes the following.“Tissue stroma is dominated by the color red. The intensity differenced, “red ratio” r=R/(R+G+B) and the red channel standard deviation σR ofimage objects may be used to classify stroma objects.” That is, stromais stained red with eosin, and therefore, the stroma objects areclassified by the color thereof.

PRIOR ART DOCUMENT Patent Document

-   Patent Document 1: JP 2010-523979 A

SUMMARY OF INVENTION Problem to be Solved by the Invention

However, when immunohistochemical staining (hereinafter referred to as“IHC staining”) is performed, nuclei of stromal cells as well as nucleiof parenchymal cells are stained blue. Thus, specifically when thestaining of cell membranes is weak, stromal cells and parenchymal cellscannot be separated from each other by the image processing described in“Background Art”.

Hence, the present invention is intended to provide an informationprocessing apparatus, an information processing system, an informationprocessing method, a program, and a recording medium, capable ofaccurately separating stromal cells and parenchymal cells from eachother regardless of the staining intensity of the cells.

Means for Solving Problem

In order to achieve the aforementioned object, the informationprocessing apparatus according to the present invention is aninformation processing apparatus including: an image processing unit forsmoothing a tissue sample image obtained by staining and then imaging abiological tissue containing parenchymal cells and stromal cells so thatluminance values of cell components of each of the parenchymal cellsbecome less than those of each of the stromal cells; and a maskgeneration unit for generating, through generating a binary image bybinarizing the tissue sample image smoothed by the image processingunit, a mask for removing a region of the stromal cells from the tissuesample image, wherein a diagnosis based on the tissue sample image issupported.

The information processing system according to the present invention isan information processing system including: the information processingapparatus according to the present invention; an input terminal; and adisplay terminal, wherein the apparatus further includes: asuperimposing unit for superimposing the mask generated by the maskgeneration unit on the tissue sample image; a counting unit for countingthe number of the parenchymal cells with each staining intensitycontained in the tissue sample image with the mask superimposed thereon,a receiving unit for receiving the tissue sample image via a network;and a sending unit for sending the number of the parenchymal cellscounted by the counting unit or the display data generated by thedisplay data generation unit via a network, and the tissue sample imagereceived by the receiving unit is input and sent, via a network, by theinput terminal, and the number of the parenchymal cells counted by thecounting unit or the display data generated by the display datageneration unit is received, via a network, and displayed by the displayterminal.

The information processing method according to the present invention isan information processing method, wherein the information processingapparatus according to the present invention is used, and the methodcomprises: an image processing step of smoothing the tissue sample imageby the image processing unit so that luminance values of cell componentsof each of the parenchymal cells become less than those of each of thestromal cells; and a mask generating step of generating, throughgenerating a binary image by binarizing the tissue sample image smoothedin the image processing step, a mask for removing a region of thestromal cells from the tissue sample image by the mask generation unit,wherein a diagnosis based on the tissue sample image is supported.

The program according to the present invention is a program capable ofexecuting the information processing method according to the presentinvention on a computer.

The recording medium according to the present invention is acomputer-readable recording medium including: the program according tothe present invention.

Effects of the Invention

According to the present invention, parenchymal cells and stromal cellscan be accurately separated from each other regardless of the stainingintensity of the cells.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a configuration of an informationprocessing apparatus according to the first embodiment of the presentinvention.

FIG. 2 is a block diagram showing a configuration of an informationprocessing system according to the second embodiment of the presentinvention.

FIG. 3 is a block diagram showing a functional configuration of apathological image diagnosis support apparatus according to the secondembodiment of the present invention.

FIG. 4A is a block diagram showing a hardware configuration of apathological image diagnosis support apparatus according to the secondembodiment of the present invention.

FIG. 4B is a block diagram showing another hardware configuration of thepathological image diagnosis support apparatus according to the secondembodiment of the present invention.

FIG. 4C is a block diagram showing yet another hardware configuration ofthe pathological image diagnosis support apparatus according to thesecond embodiment of the present invention.

FIG. 5 is a flowchart showing a procedure for operating the pathologicalimage diagnosis support apparatus according to the second embodiment ofthe present invention.

FIG. 6 is a figure showing an example of a tissue sample image to bediagnosed in the second embodiment.

FIG. 7 is a flowchart showing a procedure for generating a mask and asuperimposed image according to the second embodiment of the presentinvention.

FIG. 8 is a figure showing an example of one area selected from FIG. 6.

FIG. 9 shows a figure showing an example of reducing the size of theselected area of FIG. 8 and a figure showing an example of smoothing theselected area.

FIG. 10 is a figure showing an example of a luminance value histogramgenerated based on the lower figure of FIG. 9 and an example of abinarization threshold value calculated based on the luminance valuehistogram.

FIG. 11 shows a figure showing an example of a result obtained bybinarizing the lower figure of FIG. 9 with the threshold value shown inFIG. 10 and a figure showing an example of post-processing the resultobtained by the binarization.

FIG. 12 is a figure showing an example of a result obtained by maskingthe selected area of FIG. 8 with a mask generated based on the lowerfigure of FIG. 11.

FIG. 13 is a flowchart showing a procedure for counting the number ofcells according to the second embodiment of the present invention.

FIG. 14 is a flowchart showing a procedure for generating a bar-graphaccording to the second embodiment of the present invention.

FIG. 15 is a figure showing a configuration of storing data in the caseof counting the number of cells in the selected area with a mask of FIG.12 and then generating a bar-graph by processes of the secondembodiment.

FIG. 16 is a flowchart showing a procedure for generating display dataaccording to the second embodiment of the present invention.

FIG. 17 is a figure showing the first display example of display datagenerated by processes of the second embodiment.

FIG. 18 is a figure showing the second display example of display datagenerated by processes of the second embodiment.

FIG. 19 is a figure showing the third display example of display datagenerated by processes of the second embodiment.

FIG. 20 is a figure showing the fourth display example of display datagenerated by processes of the third embodiment.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention are illustrated in detail belowwith reference to figures. Components described in the followingembodiments, however, are mere examples, and the technical scope of thepresent invention is not limited only thereby. In the embodiments, theconcept of cell component widely encompasses components configuring acell such as a cell nucleius, a cell membrane, cytoplasm, andlymphocyte.

First Embodiment

An information processing apparatus 100 according to the firstembodiment of the present invention is described with reference toFIG. 1. FIG. 1 shows the information processing apparatus 100 forsupporting a diagnosis based on a tissue sample image 150 obtained byimmunostaining and then imaging a biological tissue containingparenchymal cells 151 and stromal cells 152. This information processingapparatus 100 includes: an image processing section (image processingunit) 110 for smoothing a tissue sample image 155 in a smoothing section(smoothing unit) 111 so that luminance values of cell components of eachof the parenchymal cells become less than those of each of the stromalcells. The information processing apparatus 100 further includes: a maskgeneration section (mask generation unit) 120 for generating, throughgenerating, in a binarization section (binarization unit) 121, a binaryimage by binarizing the tissue sample image 115 smoothed in the imageprocessing section 110, a mask 125 for removing a stromal region fromthe tissue sample image 115. With this configuration, a mask whichaccurately separates the parenchymal cells 151 and the stromal cells 152from each other can be generated regardless of the staining intensity ofthe cells.

Second Embodiment

An information processing system 250 according to the second embodimentof the present invention is described with reference to FIGS. 2 to 19.The information processing system 250 according to the presentembodiment is a system intended to exclude a stromal part in a tissuesample image that has been stained (e.g., immunostaining, IHC stainingin the present embodiment) and count the number of cancer cells in orderto select a therapy for the cancer as accurately as possible. In thepresent embodiment, information on staining of cell nuclei and cellmembranes as examples of cell components is used for recognizing stroma,and information on staining of cell nuclei is used for counting thenumber of cells.

Examples of the IHC staining used in the present embodiment includethree types of ER staining, PgR staining, and HER2 staining. Accordingto the ER staining and the PgR staining among them, cell nuclei ofpositive cells are stained brown, and those of negative cells remainblue as they have been firstly stained with hematoxylin. Further, cellmembranes are not stained in either of positive cells and negativecells.

According to the HER2 staining, cell membranes of positive cells arestained brown, and those of negative cells are not observed. Further,cell nuclei are in blue regardless of positive cells or negative cells.According to such tendency of staining, when cell nuclei are stainedbrown in ER staining or PgR staining or when cell membranes are stainedbrown in HER2 staining, stroma can be recognized relatively easily.Therefore, in the case of HER2 positive, not only information on cellnuclei, but also information on cell membranes in brown is used in thebinarization. Even though cell nuclei are not stained brown in ERstaining or PgR staining, or cell membranes are not stained brown inHER2 staining, a region which should be focused on other than stroma isdense with cell nuclei in blue.

In either of the ER staining, the PgR staining, and the HER2 staining,the number of cells is counted by counting the number of cell nuclei.That is, the number of nuclei=the number of cells.

It is summarized as follows.

-   (Step 1) A mask is generated utilizing cell nuclei in smoothing and    binarizing.    When ER/PgR positive, information on brown nuclei and blue nuclei is    utilized.    When ER/PgR negative, information on blue nuclei is utilized.    When HER2 positive, information on brown cell membranes and blue    nuclei is utilized.    When HER2 negative, information on blue nuclei is utilized.-   (Step 2) Cell nuclei are utilized to count the number of cells.    In the case of ER/PgR, the number of brown nuclei and blue nuclei is    counted.    In the case of HER2, the number of blue nuclei is counted. Further,    the staining intensity of each cell membrane surrounding each    nucleus is determined.

<Configuration of Information Processing System According to the SecondEmbodiment>

FIG. 2 is a block diagram showing a configuration of an informationprocessing system 250 including a pathological image diagnosis supportapparatus 200 as the information processing apparatus according to thesecond embodiment. As shown in FIG. 2, the pathological image diagnosissupport apparatus 200 is connected, via a network 240, to a plurality ofclient PCs 220 connected to the respective color scanners 221 forreading (inputting) a tissue sample image thereinto. The pathologicalimage diagnosis support apparatus 200 is also connected to apathological image diagnosis center 230 for receiving an image orresults obtained through processes by the pathological image diagnosissupport apparatus 200 so that specialized physicians can analyze anddiagnose. The pathological image diagnosis support apparatus 200corresponds to the information processing apparatus according to thepresent invention. It can be said that each of the client PCs 220 andthe color scanners 221 corresponds to an “input terminal” of theinformation processing system according to the present invention. It canbe said that each of the client PCs 220 also corresponds to a “displayterminal” of the information processing system according to the presentinvention. It can be said that the pathological image diagnosis center230 also corresponds to a “display terminal” of the informationprocessing system according to the present invention. The network 240may be a public network including the Internet or an in-hospital LAN.

A communication control section 201 of the pathological image diagnosissupport apparatus 200 receives a tissue sample image sent from a clientPC 220 via the network 240. That is, it can be said that thecommunication control section 201 corresponds to a “receiving unit” forreceiving a tissue sample image via the network 240. An image storagesection (image storage unit) 202 stores the received tissue sampleimage. An area selection section (area selection unit) 203 selects aplurality of areas from the received tissue sample image. Images in therespective selected areas are processed in an image processing section(image processing unit) 204, and a mask for each selected area isgenerated in a mask generation section (mask generation unit) 205. Allof the selected areas are continuously processed. When the receivedtissue sample image has been already selected and is an image with aresolution with which the number of cells can be counted easily, a maskfor the entire received tissue sample image can be generated. Thereceived tissue sample image 206, a mark 207 enclosing each selectedarea, and a tissue sample image 208 of each selected area are sent to adisplay data generation section 210. The mask 209 generated in the maskgeneration section 205 is also sent to the display data generationsection 210.

The display data generation section (display data generation unit) 210generates various pieces of display data from the received tissue sampleimage 206, the mark 207 enclosing each selected area, the tissue sampleimage 208 of each selected area, and the mask 209. As the display data,desired display data is selected by the client PC 220. The selecteddisplay data is sent from the communication control section 201 to theclient PC 220 via the network 240 and is displayed on a display screen.Alternatively, the selected display data is sent to the pathologicalimage diagnosis center 230 so that specialized physicians can analyzeand diagnose. That is, it can be said that the communication controlsection 201 corresponds to a “sending unit” for sending, via the network240, the number of the parenchymal cells counted by the counting unit ordisplay data generated by the display data generation unit.

<Functional Configuration of Pathological Image Diagnosis SupportApparatus 200>

FIG. 3 is a block diagram showing configurations of the image processingsection 204, the mask generation section 205, and the display datageneration section 210 in further detail. In the present embodiment, anexample of using an IHC-stained breast cancer tissue sample image isdescribed. The present invention, however, is not limited thereby.

The image processing section 204 includes: a size-reduction section(size-reduction unit) 301; a gray-scaling section (gray-scaling unit)302; and a smoothing section (smoothing process unit) 303. Thesize-reduction section 301 reduces the size of a tissue sample image ineach selected area. The size-reduction is performed in order to fillspaces (a background color of a film on an image) specifically aroundparenchymal cells (in order to cause the tissue sample image to be densewith parenchymal cells). The reduction ratio as a parameter of thesize-reduction is determined by a resolution of each selected area, asite of the biological tissue, and the like. The size-reduced tissuesample image in each selected area is sent to a gray-scaling section302. The gray-scaling section 302 converts a color tissue sample imageinto a gray-scale image. The present embodiment shows an example of agray scale from 0 to 255. The gray-scale image is then sent to asmoothing section 303. The smoothing section 303 smooths the gray-scaleimage so that luminance values of cell nuclei and those of stromata canbe separated from each other by binarization. The present embodimentshows an example of smoothing the gray-scale image using a Gaussianfilter. The matrix size and the weighting in the case of using theGaussian filter affect the result of the smoothing. In the presentembodiment, the matrix size and the weighting as parameters of thesmoothing are selected so that, according to the density of a part ofstained stroma, the part of stained stroma in the matrix size is small,and the weighting is affected by the surrounding unstained part, forexample. The order of the size-reduction section 301, the gray-scalingsection 302, and the smoothing section 303 is not limited by the presentembodiment. The tissue sample image generated through being processed inthis image processing section 204 is also referred to as a processedtissue sample image. In the present embodiment, parenchymal cells andstromal cells are separated from each other based on luminance values ofcell nuclei. The present invention, however, is not limited thereby, andthey may be separated from each other according to the difference ofanother cell component (a cell nucleus, a cell membrane, a cytoplasm, alymphocyte, or the like).

The mask generation section 205 includes: a binarization section(binarization unit) 304; a luminance value histogram generation section(luminance value histogram generation unit) 305; a threshold valuecalculation section (threshold value calculation unit) 306; and apost-processing section (post-processing unit) 307. An image which hasbeen image-processed in the image processing section 204, specificallyan image which has been smoothed in the smoothing section 303 so thatstromata and cell nuclei can be separated from each other is binarizedin the binarization section 304. A threshold value for this binarizationis calculated in the threshold value calculation section 306 based on aluminance value histogram generated in the luminance value histogramgeneration section 305. In the present embodiment, dynamic programming(DP) is utilized as a preferred example of calculating a threshold valuein the threshold value calculation section 306. The present invention,however, is not limited thereby, and any of the other threshold valuecalculation methods can be utilized. According to the studies by theinventors of the present invention, reliable threshold values obtainedthrough various kinds of IHC staining are, for example, from 190 to 215when the gray-scale is from 0 to 255. Thus, in the present embodiment,when a threshold value calculated by dynamic programming (DP) is lessthan 190, it is corrected to 190, and when it exceeds 215, it iscorrected to 215.

In the binarization section 304, binarization is performed using thethreshold value calculated in the threshold value calculation section306, and a part in which luminance values are higher than the thresholdvalue is used as a region which is a candidate of a mask (hereinafterreferred to as a mask candidate region). In the post-processing section307, the mask candidate region is subjected to various processes, sothat a final mask reliable as a mask is generated. For example, manydiscrete points remain around the boundary between a parenchymal cellregion to be observed and a stromal cell region in the mask candidateregion of the binary image output from the binarization process section304. This mask is intended to easily count the number of parenchymalcells to be observed. Thus, the parenchymal cells to be observed shouldbe avoided from being removed by the mask. Therefore, in thepost-processing section 307, for example, the discrete points areconnected to the parenchymal cell region to be observed as much aspossible. The discrete points are connected preferably by opening andclosing, specifically preferably closing of black indicating parenchymalcells. Moreover, for example, many isolated points are scattered overthe mask candidate region. Thus, it is preferred that the isolatedpoints are removed in the post-processing section 307. For example,noises of the isolated points are removed by dilating a white region inthe mask candidate region after the size reduction. Moreover, forexample, unnecessary holes appear in the parenchymal cell region to beobserved. When these holes are left as they are, they become parts ofthe mask. Thus, it is preferred that the holes are filled by closing inthe black parenchymal cell region or the like in the post-processingsection 307. Appropriate values are selected as parameters of thepost-processing. As mentioned above, in the present embodiment,conditions under which the parenchymal cells to be observed are notremoved are given high priority in selection of the values.

The display data generation section (display data generation unit) 210includes: a superimposing section (superimposing unit) 308; a countingsection (counting unit) 309; a bar-graph generation section (bar-graphgeneration unit) 311; a mapping section (mapping unit) 312; and adisplay data storage section (display data storage unit) 313. As shownin FIG. 2, the received tissue sample image 206, the mark 207 enclosingeach selected area, the tissue sample image 208 of each selected area,and the mask 209 are input into the display data generation section 210in order to generate display data. The display data generation section210 can be configured so that other display data is generated usingother data according to a request from the client PC 220. In the presentembodiment, five pieces of display data are provided in the display datastorage section 313. The first display data 321 is data of an imageobtained by superimposing the mark 207 enclosing each selected area onthe received tissue sample image 206 received in the mapping section 312(see FIG. 6). The second display data 322 is data of the tissue sampleimage 208 of each selected area (see FIG. 8). The third display data 323is data of an image obtained by superimposing the mask 209 as a negativeon the tissue sample image 208 of each selected area in a superimposingsection 308 (see FIG. 12). The fourth display data 324 shows valuesobtained by counting the number of cells with each staining intensity inthe parenchymal cell region to be observed of the third display data 323in the counting section 309 (see a part indicated by the numeral “1904”of FIG. 19). The fifth display data 325 is data of an image of abar-graph generated in the bar-graph generation section 311 based on thecount value according to each staining intensity (see FIGS. 17, 18, 20).The five pieces of display data provided in the display data storagesection 313 are sent and displayed as a request from the client PC 220or a service of the pathological image diagnosis support apparatus 200in combination. In the present embodiment, the superimposing section(superimposing unit) 308 and the counting section (counting unit) 309are incorporated in the display data generation section (display datageneration unit) 210. The configuration of the information processingapparatus according to the present invention, however, is not limitedthereby. In the present embodiment, the first display data 321 to thefifth display data 325 are used as display data as described above, andthe present invention, however, is not limited thereby. For example, inthe case where the number of parenchymal cells counted in the countingsection 309 is merely displayed, it is not necessary to provide a unitcorresponding to the “display data generation unit”.

<Hardware Configuration of Pathological Image Diagnosis SupportApparatus 200>

FIG. 4A is a block diagram showing a hardware configuration of thepathological image diagnosis support apparatus 200 as an informationprocessing apparatus according to the second embodiment. As shown inFIG. 4A, the pathological image diagnosis support apparatus 200includes: a CPU (Central Processing Unit) 410; a ROM (Read Only Memory)420; a communication control section 201; a RAM (Random Access Memory)430; and a storage 440.

In FIG. 4A, the CPU 410 is an arithmetic and control processor andexecutes programs so that functions of sections of FIGS. 2 and 3 can beachieved. The ROM 420 stores fixed data such as initial data and initialprograms and programs. As described for FIG. 2, the communicationcontrol section 201 communicates, via the network 240, with client PCs220 and the pathological image diagnosis center 230 etc. which areoutside devices.

The RAM 430 is used as a working area for temporal storage by the CPU410. The RAM 430 reserves a region for storing the following datanecessary to achieve the present embodiment. That is, the RAM 430includes a region for temporary storing image data 431 to be subjectedto various processes according to the present embodiment and displaydata 432 to be sent to a client PC 220 via the communication controlsection 201. The RAM 430 further includes a program execution region433.

The storage 440 is a nonvolatile storage of diagnosis supportinformation 441, various parameters 442, and various programs 443.

As shown in FIG. 4B, the image data 431 to be processed in the RAM 430includes the following data.

a tissue sample image 451 received via the communication control section201

an image 452 of one area selected from the received tissue sample image

a reduced-size image 453 obtained by reducing the size of the image ofthe selected area so as to fill spaces therein

a gray-scale image 454 obtained by converting the reduced-size image 453into gray-scale

a smoothed image 455 obtained by smoothing the gray-scale image 454

a luminance value histogram 456 generated from the smoothed image 455

a binarization threshold value 457 for binarization, calculated based onthe luminance value histogram 456

a binary image 458 obtained by binarizing the gray-scale image

a mask image 459 for deleting a stromal region, obtained bypost-processing the binary image such as connection of discrete points,deletion of isolated points, and filling of holes

The display data 432 includes the following data.

a selected area-mapped image 461 obtained by mapping the selected areainto the received tissue sample image

the first selected area image 462 of the first selected area

the first mask-superimposed image 463 obtained by masking the firstselected area image 462 with the mask image

the first cell count value 464 as the counted number of cancer cellswith each staining intensity in the mask-superimposed image

the first bar-graph image 465 generated based on the counted number ofcancer cells with each staining intensity

The same kind of data is included in display data 432 for each ofsubsequent selected areas.

As shown in FIG. 4C, the diagnosis support information 441 in thestorage 440 includes the following data.

a received tissue sample image 471

the position and size 472 of the selected area as a partial regionselected from the tissue sample image

the count value 473 relating to cancer cells in the selected area

processed display data 474 stored so as to be searchable by the tissuesample image, the patient, the case, and the like

As shown in FIG. 4C, various parameters 442 in the storage 440 includethe following parameters.

a reduction ratio 481 for use in size-reduction

a gray-scale parameter 482 for use in changing into a gray-scale image

a smoothing parameter 483 for use in smoothing, such as the matrix sizeand the weighting in a process using a Gaussian filter

a threshold value calculation parameter 484 for calculating abinarization threshold value

The maximum value and the minimum value of the threshold value arestored in the storage 440 in addition to parameters for dynamicprogramming.

a post-processing parameter 485 for post-processing such as connectionof discrete points, deletion of isolated points, and filling of holes

a staining intensity determination parameter 486 for determining theextent of staining of cell membrane in each cell by IHC staining

In the case where a score (on a scale of 0, +1, +2, and +3) isdetermined from the proportion of the number of cells with each stainingintensity, the score is also stored.

As shown in FIG. 4C, various programs 443 in the storage 440 includesthe following programs.

a diagnosis support program 491 for supporting diagnosis

an area selection program 492 for selecting a cancer cell area from thetissue sample image

an image processing program 493 for achieving processes in the imageprocessing section 204 (for executing S701 to S709 of FIG. 7)

a mask generation program 494 for achieving processes in the maskgeneration section 205 (for executing S711 to S719 of FIG. 7)

a cell count program 495 for counting the number of cells with eachstaining intensity in the image of the selected area with a mask (forexecuting S509 of FIG. 5 (specifically see FIG. 13)

a bar-graph generation program 496 for generating a bar-graph showingthe proportion of the number of cells from the counted number of cellswith each staining intensity (executing S511 of FIG. 5 (specifically seeFIG. 14)

a send data generation program 497 for generating display data for aservice to a client PC 220 via the network 240 (for executing S515 ofFIG. 5 (specifically see FIG. 16)

<Procedure for Operating Pathological Image Diagnosis Support Apparatus200>

A procedure for operating a pathological image diagnosis supportapparatus 200 having the above-described configuration is described indetail below with reference to flowcharts and examples of displayscreens. A CPU 410 executes programs shown in each flowchart so thatfunctions of the components in FIGS. 2 and 3 are achieved.

(Procedure for Supporting Diagnosis)

FIG. 5 is a flowchart showing an overall procedure for supportingdiagnosis in the present embodiment.

In a step S501 (receiving step), the pathological image diagnosissupport apparatus 200 waits for a tissue sample image to be sent from aclient PC 220. When the pathological image diagnosis support apparatus200 receives a tissue sample image, it is stored in a step S503. Then,in a step S505, the predetermined number of areas are selected from thereceived tissue sample image.

FIG. 6 shows an example of displaying the selected areas on the tissuesample image. A display screen 600 of FIG. 6 includes: a received tissuesample image 601; and a reduced-size image 602 thereof. The numeral 603indicates the selected areas for each of which a mask is generated inthe present embodiment. In FIG. 6, automatically selected five areasindicated by the numeral “1” to “5” are shown. A known method can beused as an algorithm for selecting areas, or areas may be selected by auser with the client PC 220. The display on this display screen 600 maybe sent to the client PC 220 so that a user can check the display, andthis, however, is not included in FIG. 5.

Thereafter, each of the selected areas is processed in steps S507 toS513 until all of the selected areas are processed. First, in the stepS507 (a mask generating step and a superimposing step), a mask forremoving stroma is generated for a tissue sample image of each selectedarea, and the generated mask is superimposed on the tissue sample imageso as to mask a stromal part, so that a superimposed image is generated(FIG. 7). In the step S509 (counting step), the number of cells witheach staining intensity in the tissue sample image of each selected areawith the mask is counted (FIG. 13 described below). In the step S511 (apart of a display data generating step), a bar-graph showing theproportion of the number of cells with each staining intensity,determined in the step S509 is generated (FIG. 14). Any of variousmethods can be used as a method for distinguishing staining intensitywhen the proportion of the number of cells with each staining intensityis shown. In the present embodiment, a bar-graph in which eachproportion is color-coded with a different color is generated.Subsequently, in the step S513, whether or not all of the selected areasare processed completely is determined, and if any of the selected areashas not been processed yet, the steps from S507 are performed.

When all of the selected areas are processed completely, display data isgenerated in a step S515 (the other part of a display data generatingstep) (FIG. 16). In a step S517 (sending step), the display datagenerated in the step S515 is sent to the client PC 220 (or thepathological image diagnosis center 230) via the network 240. In a stepS519, whether or not the processes are completed is determined, and ifanother piece of display data is required, display data is generated andsent in the step S515.

(Procedure for Generating Mask and Superimposed Image S507)

FIG. 7 is a flowchart showing a detailed procedure for generating a maskand a superimposed image shown in the step S507 of FIG. 5.

First, in a step S701, a tissue sample image of one selected area isacquired. FIG. 8 is an enlarged view displayed when the selected areaindicated by the numeral “3” (hereinafter referred to as the “thirdselected area”) is selected among the five selected areas 603 of FIG. 6.The third selected area 800 includes: a region 801 of cancer parenchymalcells observed as a dense mass (hereinafter referred to as a “cancerparenchymal cell region”); a region 802 of stromal cells in which darkdiscrete points are spread (hereinfater referred to as a “stromal cellregion”); and a region 803 of background which is not the tissue sampleimage (hereinafter referred to as a “background region”) (e.g., apreparation in the case of using a microscope). FIG. 8 is ablack-and-white image. Actually, however, the region 801 is a regiondense with nuclei stained light blue and cell membranes around thenuclei, stained brown, and the region 802 is a region in whichlymphocytes and the like stained deep blue are spread. The stainingintensity of the cell membrane is described in the next sectiondescribing counting the number of cells.

In a step S703 of FIG. 7, the acquired tissue sample image of theselected area is stored. A seriese of processes in the subsequent stepsS705 to S709 corresponds to a seriese of processes in the imageprocessing section 204. In the step S705 (size-reducing step), the sizeof the acquired tissue sample image of the selected area is reducedspecifically in order to fill spaces in the cancer paranchymal cellregion 801 of the tissue sample image other than cells. Then, in thestep S707 (gray-scaling step), a color image is convereted into agray-scale image, for example, in the luminance value from “0” to “255”in the present embodiment. The upper image 910 of FIG. 9 is an imageobtained after the size-reduction and the conversion into the gray-scaleimage. Thereafter, in the step S709 (image processing step), the upperimage 910 of FIG. 9 is smoothed (using a Gaussian filter in the presentembodiment), so that the image is changed to the lower image 920 of FIG.9. In the lower image 920 of FIG. 9, the contrast in the cancerparenchymal cell region 801 is decreased by the smoothing, so that theregion becomes a mass. The stromal cell region 802 is affected bysurrounding pixels having high luminance values, so that luminancevalues of spreading stromal cell nuclei staind deep blue are changed tohigh.

A series of processes in subsequent steps S711 to S719 corresponds to aseriese of processes in the mask generation section 205. First, in thestep S711 (luminance value histogram generating step), a luminance valuehistogram based on the smoothed image 920 is generated. FIG. 10 shows anexample of a luminance value histogram 1000 generated based on the lowerimage 920 of FIG. 9. The horizontal axis indicates the luminance valuefrom “0” to “255”, and the vertical axis indicates the number of pixels.As shown in FIG. 10, in the present embodiment, the luminance values inthe image 920 are from around “80” to around “230”. A peak 1010 at thenumber of pixels having the highest luminance value (the brightest)indicates the background region 803 (in a color of the preparation in apart which is not a tissue). A peak 1020 having a luminance value nextto the highest luminance value (light color) indicates the stromal cellregion 802. A part 1030 having a low luminance value (deep color)indicates a cancer parenchymal cell region 801. A peak 1040 in the part1030 indicates cell nucleus of parenchymal cell. In the step S713(threshold value calculating step), a favorable binarization thresholdvalue for separating the stromal cell region 802 from the cancerparenchymal cell region 801 is calculated by dynamic programming. In thepresent embodiment, a binarization threshold value is calculated bydynamic programing, and the present invention, however is not limitedthereby. The difference in luminance value between the stromal cellregion 802 and the cancer parenchymal cell region 801 is significantlybig by the smoothing and the like. Therefore, even though a calculatedthreshold value is different according to the method of calculating athreshold value, the stromal cell region 802 and the cancer parenchymalcell region 801 can be separated from each other by binarization.

In the step S715 (binarizing step), the tissue sample image of theselected area obtained after the smoothing is binarized using thethreshold value calculated in the step S713. The upper image 1110 ofFIG. 11 shows a tissue sample image of the selected area obtained afterthe binarization. The numeral 1111 indicates a cancer parenchymal cellregion having “0” (black) by binarization, the numeral 1112 indicates astromal cell region having “255” (white) by binarizaiton. In a binaryimage which is the upper image 1110 of FIG. 11, discrete points 1113spread from the mass of parenchymal cells remain between the parenchymalcell region and the stromal cell region. Moreover, isolated points 1114remain in the white stromal cell region, and small holes 1115 remain inthe black parenchymal cell region. In the step S717 (post-processingstep), post-processing is performed in order to connect or delete thesespecific points.

In order to connect the discrete points 1113, opening and closing areperformed. In order to remove the isolated points 1114, the white regionis dilated. In order to fill the small holes 1115, the holes in theblack region is filled by closing or the like. Which size of black partis regarded as a discrete point or an isolated point, or which size ofwhite part is regarded as a hole can be selected based on empiricalvalues thereof, for example. Specifically, for example, appropriateparameters can be selected based on the zoom ratio or the resolution ofimage and empirical values thereof. Basically, since the purpose is tocount the number of cells with each staining intensity in a black region(which is not masked), parameters are decided so that a region includingcells is not masked, and the black region remains. The lower image 1120of FIG. 11 shows an example of an image obtained after post-processing.In FIG. 11, a white region indicated by the numeral 1121 is a region tobe a mask. As can be seen from the comparison with the upper image 1110of FIG. 11, the discrete points 1113 are connected to the black region,the isolated points 1114 are removed, and the small holes 1115 arefilled by the post processing.

In the step S719, the image obtained after the post-processing (thelower image 1120 of FIG. 11) is enlarged at the same ratio as used inthe size-reduction performed in the step S705, and a proper mask isgenerated using the white region as a mask region.

Subsequently, in a step S721, the generated mask is superimposed on theoriginal tissue sample image of the selected area, so that the maskregion is removed. Thus, a tissue sample image 1200 of the selected areawith the mask is generated as shown in FIG. 12. In a step S723, thetissue sample image 1200 of the selected area with the mask is stored inassociation with the selected area ID by which the selected area isdistinguished.

(Procedure for Counting the Number of Cells S509)

FIG. 13 is a flowchart showing a detailed procedure for counting thenumber of cells shown in S509 of FIG. 5.

First, in a step S1301, a mask-superimposed image (see FIG. 12) storedin S723 of FIG. 7 is acquired. In a step S1303, whether the receptor inthe IHC staining of the present embodiment is “HER2” or any of “ER” and“PgR” is determined. When the receptor is “HER2”, the cell membrane isstained, so that the change in staining intensity can be observed. Thus,in a step S1305, the number of cells in each of three categories ofstaining intensity (none, weak, and strong) is counted in the presentembodiment. The “none” indicates a cell in which a cell nucleus isstained blue, and a cell membrane is not stained. The “weak” indicates acell in which a part of a cell membrane is stained brown. The “strong”indicates a cell in which the whole cell membrane is stained and have aclosed curve. In a step S1307, the counted number of cells with eachstaining intensity is stored in association with the staining intensity.

When the receptor is “ER” or “PgR”, cell nuclei are stained, so that thestaining is categorized into “present” or “none”. Therefore, in a stepS1309, the number of cells in each of the presence and absence of thestaining of cell nuclei is counted. Then, in a step S1311, the number ofcells is stored in association with the presence or absence of thestaining.

(Procedure for Generating Bar-Graph S511)

FIG. 14 is a flowchart showing a detailed procedure for generating abar-graph indicated by S511 of FIG. 5.

First, in a step S1401, whether the receptor in the IHC staining of thepresent embodiment is “HER2” or any of “ER” and “PgR” is determined.When the receptor is “HER2”, the proportion of the number of cells ineach of the three categories of staining intensity is calculated in astep S1403. In the present embodiment, the proportion of the number ofcells with each staining intensity is indicated by percentage, assumingthat the proportion of the total number of cells is 100%. Subsequently,in a step S1405, a bar-graph in which each proportion is color-coded sothat three categories of staining intensity are distinguished from oneanother is generated. In the present embodiment, a bar-graph in whichthe proportion of the number of cells in “none” of the staining isindicated by green, that in “weak” of the staining is indicated byyellow, and that in “strong” of the staining is indicated by red in thisorder (none: green—weak: yellow—strong: red) is generated.

When the receptor is “ER” or “PgR”, the proportion of the number ofcells in each of the presence and absence of the staining is determinedin a step S1407, and a bar-graph in which the proportion of the numberof cells in “none” of the staining is indicated by green, and that inthe “presence” of the staining is indicated by red in this order (none:green—presence: red) is generated in a step S1409. In a step S1411, thegenerated bar-graph is stored in association with the selected area ofthe tissue sample image.

The bar-graph added to the display data is useful as auxiliaryinformation in the case where a score of reactivity of cell membrane inthe selected area of the tissue sample image is determined. For example,the score (+3, +2, +1, 0) of HER2 is determined according to theproportion of the number of cells with strong staining intensity, acombination of the proportion with strong staining intensity and thatwith weak staining intensity, and the like as determination criteria.Therefore, if the proportion or the combination is determined oraccessorily determined visually, it is useful in diagnosis based on thetissue sample image. Thus, the present embodiment shows an example ofnormalizing by the percentage. If it is necessary to check the totalnumber of cells, the proportion thereof may be shown with indicatingeach actual number of cells by a different color.

Even though automatic score determination is not mentioned in thepresent embodiment, a score of each selected area and a score of theentire tissue sample image as a result thereof may be automaticallydetermined.

(Configuration of Display Data Storage Section 313)

FIG. 15 is a figure showing a configuration of the display data storagesection 313 of FIG. 3. The configuration of the display data 432 in theRAM 430 and that of the display data 474 in the storage 440 may besimilar to the respective configurations of FIG. 15.

FIG. 15 shows a configuration of one received tissue sample image 310.As shown in FIG. 15, the tissue sample image 130 includes: a pluralityof selected area IDs 1510 and 1520 in association with a tissue sampleimage ID 1500. The tissue sample image 310 also holds various pieces ofimage data 1530 and 1540 (a tissue sample image without a mask, a tissuesample image with a mask, and the like) in association with selectedarea IDs 1510 and 1520. The tissue sample image 310 further stores: thetotal counted number of cells 1511; the number of cells with stainingintensity (strong) and the proportion thereof 1512; the number of cellswith staining intensity (weak) and the proportion thereof 1513; and thenumber of cells with staining intensity (none) and the proportionthereof 1514 in association with the selected area ID 1510. The tissuesample image 310 further stores: a bar-graph image 1550 generatedthrough being pointed with a bar-graph image pointer. In the bar-graphimage 1550, the length of the red bar 1551, the length of the yellow bar1552, and the length of the green bar 1553 correspond to the proportionwith staining intensity (strong), the proportion with staining intensity(weak), and the proportion with staining intensity (none), respectively.

In the present embodiment, data based on one received tissue sampleimage is managed in the display data storage section 313, and thepresent invention, however, is not limited thereby. For example, aplurality of images stored according to a diagnosis history may bemanaged by the patient ID, the hospital ID, the case ID, and the likethrough including the diagnosis history in data stored in the displaydata storage section 313.

(Procedure for Generating Display Data)

FIG. 16 is a flowchart showing a detailed procedure for generatingdisplay data indicated by S515 of FIG. 5. The display data may bepreviously provided as a service of the pathological image diagnosissupport apparatus as in the present embodiment or may be generated inresponse to a request from a client PC 220 by a user. In the lattercase, display data is generated dialogically, and this, however, is notdescribed in detail.

In a step S1601, whether or not the entire received tissue sample imageis displayed in S515 is determined. When the entire received tissuesample image is displayed, selected areas are mapped into the tissuesample image in a step S1603, and a bar-graph is added to a thumbnailimage of each selected area. Further, an image indicating the scores inthe tissue sample image by color borders is added. Such display image isgenerated as display data.

FIG. 17 is a figure showing a display screen 1700 obtained by displayingthe display data generated in the step S1603 on a client PC 220. Asshown in FIG. 17, the display screen 1700 includes: a tissue sampleimage 1701 with five selected areas superimposed thereon; a reduced-sizeimage 1702 of the tissue sample image; a thumbnail image 1703 of thetissue sample image with color borders according to the respectivescores; and thumbnail images 1704 of the the respective five selectedareas each with a bar-graph. This thumbnail images 1703 and 1704 serveas buttoms for selecting a display image by a user.

In a step S1605, whether or not one selected area is displayed in S515is determined. When one selected area is displayed, an image obtained byadding a bar-graph corresponding to the selected area to an enlargedimage of the one selected area and adding a bar-graph to a thumbnailimage of each selected area is generated as display data in a stepS1607.

FIG. 18 is a figure showing a display screen 1800 obtained by displayingthe display data generated in the step S1607 on a client PC 220. Asshown in FIG. 18, the display screen 1800 includes: an enlarged tissuesample image 1801 of one selected area, a reduced-size image 1802 of theselected area; and a bar-graph 1803 corresponding to the selected area.A thumbnail image 1703 of the entire tissue sample image and thumbnailimages 1704 of five selected areas each with a bar-graph are displayedas buttons. FIG. 18 shows an example of a case where the third selectedarea indicated by the numeral “3” in FIG. 17 is selected, so that adark-color border of the third selected area indicated by the numeral“3” among the thumbnail images of the five selected areas indicates theresult that the third selected area is selected. The bar-graph 1803corresponding to the third selected area indicated by the numeral “3” isthe same as the bar-graph image 1550. The numeral 1804 corresponds tothe numeral 1551 of FIG. 15. The numeral 1805 corresponds to the numeral1552 of FIG. 15. The numeral 1806 corresponds to the numeral 1553 ofFIG. 15.

In a step S1609, whether or not the counted number of cells with eachstaining intensity and the distribution thereof in one selected area isdisplayed in S515 is determined. When the counted number of cells andthe distribution are displayed, in a step S1611, an image obtained bycolor-coding cells in an enlarged image of the one selected areaaccording to each staining intensity is generated, and an image obtainedby adding the counted number of cells with each staining intensity tothe image thus obtained is generated as display data.

FIG. 19 is a figure showing a display screen 1900 obtained by displayingthe display data generated in the step S1611 on a client PC 220. Asshown in FIG. 19, the display screen 1900 includes: an enlarged tissuesample image of one selected area; and the counted number of cells 1904with each staining intensity. In the enlarged tissue sample image of theone selected area, cells are color-coded with different colors accordingto each staining intensity. The numeral 1901 indicates cells withstaining intensity (strong) colored with red, the numeral 1902 indicatescells with staining intensity (weak) colored with yellow, and thenumeral 1903 indicates cells with staining intensity (none) colored withgreen.

In a step S1613, whether or not a mask-superimposed image obtained bymasking the tissue sample image of the selected area with a generatedmask is displayed in S515 is determined. When the mask-superimposedimage is displayed, a mask-superimposed image (see FIG. 12) isincorporated into display data in a step S1615. An example of a displayin the case where the mask-superimposed image is incorporated intodisplay data is not specifically shown. The mask-superimposed image maybe displayed as substitute for the tissue sample image of the selectedarea or may be added to a region of the display screen. Themask-superimposed image can be compared with an image without a mask bydisplaying the mask-superimposed image, so that whether or not countingof the number of cells with each staining intensity is performedcorrectly can be checked.

When none of combinations of pieces of display data is selected in thesteps S1601, S1605, and S1609, any of the other combinations of piecesof display data and a combination requested from a client PC 220 by auser is provided in a step S1617.

Third Embodiment

The third embodiment of the present invention is described withreference to FIG. 20. In the second embodiment, the pathological imagediagnosis support apparatus 200 automatically selects areas, forexample, five areas, and the number of cells with each stainingintensity is counted. In the third embodiment, as shown in FIG. 20,areas for counting the number of cells with each staining intensity canbe selected by a user besides areas automatically selected by thepathological image diagnosis support apparatus 200.

Areas 2005 automatically selected by the pathological image diagnosissupport apparatus 200 and areas 2006 selected by a user are displayed ona display screen 2000 of FIG. 20 in the form of being superimposed onthe sent tissue sample image 2001. For example, the selected areas canbe input from a client PC 220 in response to a display of a screen shownin FIG. 6 on a client PC 220 before counting the number of cells witheach staining intensity or in response to a display of a result shown inFIG. 17 after conting the number of cells with each staining intensity.

Other Embodiments

The embodiments of the present invention are described in detail above.The scope of the present invention encompasses any system and apparatusobtained by combining characteristics of the embodiments.

The present invention may be applied to a system composed of a pluralityof units or a single unit. The present invention is applicable also inthe case where the control program for achieving the functions of theembodiments is supplied from a system or apparatus directly or remotelyand executed. Therefore, the scope of the present invention encompassesa control program installed in a computer so as to achieve functions ofthe present invention, a storage medium storing the control program, anda WWW server from which the control program is downloaded.

While the invention has been particularly shown and described withreference to exemplary embodiments thereof, the invention is not limitedto these embodiments. It will be understood by those of ordinary skillin the art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the present invention asdefined by the claims.

This application is based upon and claims the benefit of priority fromJapanese patent application No. 2010-223049, filed on Sep. 30, 2010, thedisclosure of which is incorporated herein in its entirety by reference.

EXPLANATION OF REFERENCE NUMERALS

-   100 information processing apparatus-   110 image processing section (image processing unit)-   111 smoothing section (smoothing unit)-   115 tissue sample image-   120 mask generation section (mask generation unit)-   121 binarization section (binarization unit)-   125 mask-   150 tissue sample image-   151 parenchymal cell-   152 stromal cell-   155 tissue sample image-   200 pathological image diagnosis support apparatus (information    processing apparatus)-   201 communication control section (receiving unit, sending unit)-   202 image storage section (image storage unit)-   203 area selection section (area selection unit)-   204 image processing section (image processing unit)-   205 mask generation section (mask generation unit)-   206 received tissue sample image-   207 mark enclosing selected area-   208 tissue sample image of each selected area-   209 mask-   210 display data generation section (display data generation unit)-   220 client PC (input terminal, display terminal)-   221 color scanner (input terminal)-   230 pathological image diagnosis center (display terminal)-   240 network-   250 information processing system-   301 size-reduction section (size-reduction unit)-   302 gray-scaling section (gray-scaling unit)-   303 smoothing section (smoothing unit)-   304 binarization section (binarization unit)-   305 luminance value histogram generation section (luminance value    histogram generation unit)-   306 threshold value calculation section (threshold value calculation    unit)-   307 post-processing section (post-processing unit)-   308 superimposing section (superimposing unit)-   309 counting section (counting unit)-   311 bar-graph generation section (bar-graph generation unit)-   312 mapping section (mapping unit)-   313 display data storage section (display data storage unit)-   321 first display data-   322 second display data-   323 third display data-   324 fourth display data-   325 fifth display data-   410 CPU-   420 ROM-   430 RAM-   431 image data to be processed-   432 display data-   433 program execution region-   440 storage-   441 diagnosis support information-   442 various parameters-   443 various programs-   451 tissue sample image received via communication control section    201-   452 image of one area selected from received tissue sample image-   453 reduced-size image obtained by reducing size of image of    selected area so as to fill spaces therein-   454 gray-scale image obtained by converting reduced-size image 453    into gray-scale-   455 smoothed image obtained by smoothing gray-scale image 454-   456 luminance value histogram generated from smoothed image 455-   457 binarization threshold value for binarization, calculated based    on luminance value histogram 456-   458 binary image obtained by binarizing gray-scale image-   459 mask image for deleting stromal region, obtained by    post-processing binary image such as connection of discrete points,    deletion of isolated points, and filling of holes-   461 selected area-mapped image obtained by mapping selected area    into received tissue sample image-   462 the first selected area image of the first selected area-   463 the first mask-superimposed image obtained by masking the first    selected area image 462 with mask image-   464 the first cell count value as the counted number of cancer cells    with each staining intensity in mask-superimposed image-   465 the first bar-graph image generated based on the counted number    of cancer cells with each staining intensity-   471 received tissue sample image-   472 position and size of selected area as partial region selected    from tissue sample image-   473 count value relating to cancer cells in selected area-   474 processed display data stored so as to be searchable by tissue    sample image, patient, case, and the like-   481 reduction ratio for use in size-reduction-   482 gray-scale parameter for use in changing into gray-scale image-   483 smoothing parameter for use in smoothing-   484 threshold value calculation parameter for calculating    binarization threshold value-   485 post-processing parameter for post-processing such as connection    of discrete points, deletion of isolated points, and filling of    holes-   486 staining intensity determination parameter for determining the    extent of staining of cell membrane in each cell by IHC staining-   491 diagnosis support program for supporting diagnosis-   492 area selection program for selecting cancer cell area from    tissue sample image-   493 image processing program for achieving processes in image    processing section 204 (for executing S701 to S709 of FIG. 7)-   494 mask generation program for achieving processes in mask    generation section 205 (for executing S711 to S719 of FIG. 7)-   495 cell count program for counting the number of cells with each    staining intensity in image of selected area with mask (for    executing S509 of FIG. 5 (specifically see FIG. 13)-   496 bar-graph generation program for generating bar-graph showing    the proportion of the number of cells from the counted number of    cells with each staining intensity (executing S511 of FIG. 5    (specifically see FIG. 14)-   497 send data generation program for generating display data for    service to client PC 220 via network 240 (for executing S515 of FIG.    5 (specifically see FIG. 16)-   600 display screen-   601 received tissue sample image-   602 reduced-size image of received tissue sample image-   603 selected areas for each of which mask is generated-   800 third selected area-   801 cancer parenchymal cell region observed as dense mass-   802 stromal cell region in which dark discrete points are spread-   803 background region which is not tissue sample image-   910 image obtained after size-reduction and conversion into    gray-scale image-   920 image obtained by smoothing image 910-   1000 luminance value histogram generated based on image 920-   1010 peak relating to background region 803 (in color of a    preparation in a part which is not tissue)-   1020 peak relating to stromal cell region 802-   1030 peak relating to cancer parenchymal cell region 801-   1040 peak relating to cell nucleus of parenchymal cell-   1110 tissue sample image of selected area obtained after    binarization-   1111 cancer parenchymal cell region having “0” (black) by    binarization-   1112 stromal cell region having “255” (white) by binarization-   1113 discrete point spread from mass of parenchymal cells-   1114 isolated point-   1115 hole-   1120 image obtained after post-processing-   1121 region to be mask-   1200 tissue sample image of selected area with mask-   310 one received tissue sample image-   1500 tissue sample image ID-   1510, 1520 selected area ID-   1511 total counted number of cells-   1512 the number of cells with staining intensity (strong) and the    proportion thereof-   1513 the number of cells with staining intensity (weak) and the    proportion thereof-   1514 the number of cells with staining intensity (none) and the    proportion thereof-   1515 bar-graph image pointer-   1530, 1540 various pieces of image data-   1550 bar-graph image-   1551 red bar (the proportion with staining intensity (strong))-   1552 yellow bar (the proportion with staining intensity (weak))-   1553 green bar (the proportion with staining intensity (none))-   1700 display screen obtained by displaying display data generated in    step S1603 on client PC 220-   1701 tissue sample image with five selected areas superimposed    thereon-   1702 reduced-size image of tissue sample image-   1703 thumbnail image of tissue sample image with color borders of    scores-   1704 thumbnail images of five selected areas each with bar-graph-   1800 display screen obtained by displaying display data generated in    step S1607 on client PC 220-   1801 tissue sample image-   1802 reduced-size image of selected area-   1803 bar-graph corresponding to selected area-   1804 red bar (the proportion with staining intensity (strong))-   1805 yellow bar (the proportion with staining intensity (weak))-   1806 green bar (the proportion with staining intensity (none))-   1900 display screen when display data generated in step S1611 is    displayed on client PC 220-   1901 cells with staining intensity (strong) colored with red-   1902 cells with staining intensity (weak) colored with yellow-   1903 cells with staining intensity (none) colored with green-   1904 the counted number of cells with each staining intensity-   2000 display screen-   2001 sent tissue sample image-   2002 reduced-size image of selected area-   2003 thumbnail image of tissue sample image with color borders of    scores-   2004 thumbnail images of five selected areas each with bar-graph-   2005 areas automatically selected by pathological image diagnosis    support apparatus 200-   2006 areas selected by user

The invention claimed is:
 1. An information processing apparatuscomprising: an image processing unit for smoothing a tissue sample imageobtained by staining and then imaging a biological tissue containingparenchymal cells and stromal cells so that luminance values of cellcomponents of each of the parenchymal cells become less than those ofeach of the stromal cells; and a mask generation unit for generating,through generating a binary image by binarizing the tissue sample imagesmoothed by the image processing unit, a mask for removing a region ofthe stromal cells from the tissue sample image, wherein a valueaccording to luminance values of surroundings of the cell components ofeach of the parenchymal cells is subtracted from the luminance values ofthe cell components of each of the parenchymal cells, and a valueaccording to luminance values of surroundings of the cell components ofeach of the stromal cells is added to the luminance values of the cellcomponents of each of the stromal cells in the smoothing of the tissuesample image by the image processing unit, and wherein a diagnosis basedon the tissue sample image is supported.